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Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach
dc.contributor.authorRouhi Ardeshiri, Reza
dc.contributor.authorMa, Chengbin
dc.date.accessioned2021-09-08T14:34:20Z
dc.date.available2022-10-08 10:34:18en
dc.date.available2021-09-08T14:34:20Z
dc.date.issued2021-09
dc.identifier.citationRouhi Ardeshiri, Reza; Ma, Chengbin (2021). "Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach." International Journal of Energy Research 45(11): 16633-16648.
dc.identifier.issn0363-907X
dc.identifier.issn1099-114X
dc.identifier.urihttps://hdl.handle.net/2027.42/169255
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.othergated recurrent unit
dc.subject.otherfeature engineering
dc.subject.otherremaining useful life
dc.subject.othermultivariate time series
dc.subject.otherlithium‐ion battery
dc.titleMultivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMechanical Engineering
dc.subject.hlbtoplevelEngineering
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/169255/1/er6910.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/169255/2/er6910_am.pdf
dc.identifier.doi10.1002/er.6910
dc.identifier.sourceInternational Journal of Energy Research
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dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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